检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

20,798 篇 会议
87 篇 期刊文献
65 册 图书

馆藏范围

20,949 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,274 篇 工学
- 10,922 篇 计算机科学与技术...
- 2,484 篇 机械工程
- 2,306 篇 软件工程
- 913 篇 光学工程
- 771 篇 电气工程
- 556 篇 控制科学与工程
- 405 篇 信息与通信工程
- 210 篇 测绘科学与技术
- 131 篇 生物医学工程（可授...
- 104 篇 电子科学与技术（可...
- 100 篇 生物工程
- 92 篇 仪器科学与技术
- 56 篇 化学工程与技术
- 52 篇 建筑学
- 48 篇 土木工程
- 44 篇 安全科学与工程
- 38 篇 力学（可授工学、理...
- 38 篇 航空宇航科学与技...
- 35 篇 交通运输工程
3,457 篇 医学
- 3,449 篇 临床医学
- 34 篇 基础医学(可授医学...
2,315 篇 理学
- 1,154 篇 数学
- 1,132 篇 物理学
- 417 篇 统计学（可授理学、...
- 386 篇 生物学
- 252 篇 系统科学
- 57 篇 化学
353 篇 管理学
- 184 篇 图书情报与档案管...
- 176 篇 管理科学与工程(可...
- 32 篇 工商管理
28 篇 法学
20 篇 农学
15 篇 教育学
9 篇 经济学
8 篇 艺术学
5 篇 文学
5 篇 军事学

主题

8,203 篇 computer vision
3,010 篇 pattern recognit...
2,732 篇 training
1,769 篇 computational mo...
1,657 篇 visualization
1,483 篇 cameras
1,415 篇 shape
1,369 篇 three-dimensiona...
1,369 篇 face recognition
1,285 篇 image segmentati...
1,272 篇 feature extracti...
1,178 篇 robustness
1,090 篇 semantics
1,040 篇 layout
1,007 篇 object detection
975 篇 object recogniti...
969 篇 computer science
946 篇 computer archite...
946 篇 benchmark testin...
931 篇 codes

机构

174 篇 univ sci & techn...
154 篇 carnegie mellon ...
148 篇 univ chinese aca...
144 篇 chinese univ hon...
113 篇 microsoft resear...
103 篇 zhejiang univ pe...
99 篇 swiss fed inst t...
97 篇 tsinghua univ pe...
93 篇 tsinghua univers...
91 篇 microsoft res as...
88 篇 shanghai ai lab ...
81 篇 zhejiang univers...
76 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
69 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 google res mount...
66 篇 univ oxford oxfo...

作者

80 篇 van gool luc
71 篇 zhang lei
59 篇 timofte radu
48 篇 yang yi
47 篇 xiaoou tang
44 篇 darrell trevor
43 篇 tian qi
43 篇 luc van gool
42 篇 loy chen change
42 篇 sun jian
42 篇 li fei-fei
40 篇 qi tian
39 篇 li stan z.
37 篇 liu yang
37 篇 chen xilin
36 篇 shan shiguang
35 篇 liu xiaoming
35 篇 vasconcelos nuno
35 篇 torralba antonio
32 篇 zhou jie

语言

20,922 篇 英文
14 篇 中文
11 篇 其他
2 篇 日文
2 篇 土耳其文

检索条件"任意字段=2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009"

共 20950 条记录，以下是921-930 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Multi-View Spatial-Temporal Learning for Understanding Unusual Behaviors in Untrimmed Naturalistic Driving Videos

Multi-View Spatial-Temporal Learning for Understanding Unusu...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huy-Hung Nguyen Chi Dai Tran Long Hoang Pham Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Duong Khac Vu Quoc Pham-Nam Ho Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9798350365474

The task of Naturalistic Driving Action recognition aims to detect and temporally localize distracting driving behavior in untrimmed videos. In this paper, we introduce our framework for Track 3 of the 8th AI City Challenge in 2024. The approach is primarily based on large model fine-tuning and ensemble techniques to train a set of action recognition models on a small-scale dataset. Starting with raw videos, we segment them into individual action sequences based on their annotation. We then fine-tune four different action recognition models, with K-fold cross-validation applied to the segmented data. Following this, we execute a multi-view ensemble, selecting the most visible camera views for each action class to generate clip-level classification results for each video. Finally, a multi-step post-processing algorithm, which is designed for the AI City Challenge dataset's specific features, is employed to perform temporal action localization and produce temporal segments for the actions. Our solution achieves a final mOS score of 0.7798 and attains the 5th rank on the public leaderboard for the test set A2 of the challenge. The source code will be publicly available at https://***/SKKUAutoLab/AIC24-Track03.

关键词： action recognition

来源：评论

学校读者我要写书评

暂无评论

Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shoman, Maged Wang, Dongdong Aboah, Armstrong Abdel-Aty, Mohamed Univ Cent Florida Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL 32816 USA North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND USA Univ Cent Florida Joint Appointment Dept Comp Sci Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL USA

ISBN: (纸本)9798350365474

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The opensource code will be available at https://***/UCF-SST-Lab/AICity2024cvprW

关键词： computer vision cross-modality learning dense video captioning domain adaptation natural language processing

来源：评论

学校读者我要写书评

暂无评论

Are Deep Neural Networks SMARTer than Second Graders?

Are Deep Neural Networks SMARTer than Second Graders?

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cherian, Anoop Peng, Kuan-Chuan Lohit, Suhas Smith, Kevin A. Tenenbaum, Joshua B. Mitsubishi Elect Res Labs Cambridge MA 02139 USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, question answering (e.g., ChatGPT), etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset1, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6-8 age group. Our dataset consists of 101 unique puzzles;each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning, among others. To scale our dataset towards training deep neural networks, we programmatically generate entirely new instances for each puzzle while retaining their solution algorithm. To benchmark the performance on the SMART-101 dataset, we propose a vision-and-language meta-learning model that can incorporate varied state-of-the-art neural backbones. Our experiments reveal that while powerful deep models offer reasonable performances on puzzles in a supervised setting, they are not better than random accuracy when analyzed for generalization - filling this gap may demand new multimodal learning approaches.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

BiFormer: vision Transformer with Bi-Level Routing Attention

BiFormer: Vision Transformer with Bi-Level Routing Attention

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Lei Wang, Xinjiang Ke, Zhanghan Zhang, Wayne Lau, Rynson City Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pair-wise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into attention, such as restricting the attention operation to be inside local windows, axial stripes, or dilated windows. In contrast to these approaches, we propose a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness. Specifically, for a query, irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bi-level routing attention, which utilizes the sparsity to save both computation and memory while involving only GPU-friendly dense matrix multiplications. Built with the proposed bi-level routing attention, a new general vision transformer, named BiFormer, is then presented. As BiFormer attends to a small subset of relevant tokens in a query adaptive manner without distraction from other irrelevant ones, it enjoys both good performance and high computational efficiency, especially in dense prediction tasks. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design. Code is available at https://***/rayleizhu/BiFormer.

关键词： Deep learning architectures and techniques

来源：评论

学校读者我要写书评

暂无评论

DETRs with Hybrid Matching

DETRs with Hybrid Matching

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Jia, Ding Yuan, Yuhui He, Haodi Wu, Xiaopei Yu, Haojun Lin, Weihong Sun, Lei Zhang, Chao Hu, Han Peking Univ Beijing Peoples R China Stanford Univ Stanford CA USA Zhejiang Univ Hangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, PETRv2, PETR, and TransTrack, among others. Code is available at: https://***/HDETR.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient vision Transformers

Sparsifiner: Learning Sparse Instance-Dependent Attention fo...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wei, Cong Duke, Brendan Jiang, Ruowei Aarabi, Parham Taylor, Graham W. Shkurti, Florian Univ Toronto Toronto ON Canada Univ Guelph Guelph ON N1G 2W1 Canada Modiface Inc Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350301298

vision Transformers (ViT) have shown competitive advantages in terms of performance compared to convolutional neural networks (CNNs), though they often come with high computational costs. To this end, previous methods explore different attention patterns by limiting a fixed number of spatially nearby tokens to accelerate the ViT's multi-head self-attention (MHSA) operations. However, such structured attention patterns limit the token-to-token connections to their spatial relevance, which disregards learned semantic connections from a full attention mask. In this work, we propose an approach to learn instance-dependent attention patterns, by devising a lightweight connectivity predictor module that estimates the connectivity score of each pair of tokens. Intuitively, two tokens have high connectivity scores if the features are considered relevant either spatially or semantically. As each token only attends to a small number of other tokens, the binarized connectivity masks are often very sparse by nature and therefore provide the opportunity to reduce network FLOPs via sparse computations. Equipped with the learned unstructured attention pattern, sparse attention ViT (Sparsifiner) produces a superior Pareto frontier between FLOPs and top-1 accuracy on ImageNet compared to token sparsity. Our method reduces 48% similar to 69% FLOPs of MHSA while the accuracy drop is within 0.4%. We also show that combining attention and token sparsity reduces ViT FLOPs by over 60%.

关键词： Efficient and scalable vision

来源：评论

学校读者我要写书评

暂无评论

Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

Guiding Pseudo-labels with Uncertainty Estimation for Source...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Litrico, Mattia Del Bue, Alessio Morerio, Pietro Ist Italiano Tecnol Pattern Anal & Comp Vision PAVIS Genoa Italy

ISBN: (纸本)9798350301298

Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS with +12.3% in the single-source setting and +6.6% in multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.

关键词： continual low-shot meta or long-tail learning Transfer

来源：评论

学校读者我要写书评

暂无评论

Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual recognition Problems

Learning Common Rationale to Improve Self-Supervised Represe...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shu, Yangyang van den Hengel, Anton Liu, Lingqiao Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9798350301298

Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings.(1)

关键词： Self-supervised or unsupervised representation learning

来源：评论

学校读者我要写书评

暂无评论

METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens

METransformer: Radiology Report Generation by Transformer wi...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Zhanyu Liu, Lingqiao Wang, Lei Zhou, Luping Univ Sydney Sydney NSW Australia Univ Adelaide Adelaide SA Australia Univ Wollongong Wollongong NSW Australia

ISBN: (纸本)9798350301298

In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint diagnosis" mechanism to upgrade the existing "single expert" framework commonly seen in the current literature. To this end, we propose METransformer, a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of multiple learnable "expert" tokens into both the transformer encoder and decoder. In the encoder, each expert token interacts with both vision tokens and other expert tokens to learn to attend different image regions for image representation. These expert tokens are encouraged to capture complementary information by an orthogonal loss that minimizes their overlap. In the decoder, each attended expert token guides the cross-attention between input words and visual tokens, thus influencing the generated report. A metrics-based expert voting strategy is further developed to generate the final report. By the multi-experts concept, our model enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. Experimental results demonstrate the promising performance of our proposed model on two widely used benchmarks. Last but not least, the framework-level innovation makes our work ready to incorporate advances on existing "single-expert" models to further improve its performance.

关键词： and reasoning language vision

来源：评论

学校读者我要写书评

暂无评论

DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

DeepLSD: Line Segment Detection and Refinement with Deep Ima...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pautrat, Remi Barath, Daniel Larsson, Viktor Oswald, Martin R. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Lund Univ Lund Sweden Univ Amsterdam Amsterdam Netherlands Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9798350301298

Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://***/cvg/DeepLSD.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 89 90 91 92 93 94 95 96 97 98 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：